混行环境下网联信号交叉口车路协同控制方法  

Vehicle-infrastructure cooperative control method of connected and signalized intersection in mixed traffic environment

作　　者：王润民[1,2,3] 张心睿 赵祥模 吴霞[1,2,3] 凡海金 WANG Run-min;ZHANG Xin-rui;ZHAO Xiang-mo;WU Xia;FAN Hai-jin(Collaborative Innovation Center for Western Traffic Safety and Intelligent Control by Province and Ministry,Chang'an University,Xi'an 710018,Shaanxi,China;School of Information Engineering,Chang'an University,Xi'an 710064,Shaanxi,China;Joint Laboratory for Internet of Vehicles of Ministry of Education-China Mobile Communications Corporation,Chang'an University,Xi'an 710018,Shaanxi,China)

机构地区：[1]长安大学西部交通安全与智能控制省部共建协同创新中心,陕西西安710018 [2]长安大学信息工程学院,陕西西安710064 [3]长安大学车联网教育部-中国移动联合实验室,陕西西安710018

出　　处：《交通运输工程学报》2022年第3期139-151,共13页Journal of Traffic and Transportation Engineering

基　　金：国家重点研发计划(2021YFB2501200);陕西省重点研发计划(2021LLRH-04-01-03);中国博士后科学基金项目(2022M710483);陕西省自然科学基金项目(2022JQ-663)。

摘　　要：为了提高网联信号交叉口车路协同控制对真实交通环境的适应性,以智能网联汽车与网联人工驾驶汽车混行的典型交通应用场景为研究对象,通过构建八相位网联信号交叉口,研究了混行环境下的交通信号和网联车辆轨迹车路协同优化控制方法;在对场景中的网联车辆运动学特性和跟驰行为进行建模的基础上,构建了一种混行车辆编队方法;基于混行车队模型、安全约束与燃油消耗模型,建立了基于滚动优化的交通信号-车辆轨迹协同优化控制方法;基于异步分层优化思路,将该协同控制问题分解为上层交通信号优化与下层车辆轨迹优化两方面,以交叉口车辆行驶延误时间和燃油消耗量为优化目标,利用遗传算法和“三段式”轨迹优化法分别对交通信号优化问题与车辆轨迹优化问题进行求解;对不同稳态车速与智能网联汽车渗透率下构建的混行交通流的稳定性进行了验证,并通过仿真测试分析了所提出的协同优化控制方法的控制效能与关键参数对控制效能的影响。分析结果表明:在不同交通流量与智能网联汽车渗透率下,提出的控制方法均可有效提升交叉口通行效率与燃油经济性;在完全渗透环境下,较固定配时交通信号控制方法最高可分别提升57.3%和13.3%;随着智能网联汽车渗透率的增加,其控制效能不断提高,较无渗透条件最高可分别提升42.0%和14.2%;即使智能网联汽车渗透率仅达到20%,较无渗透条件也可以在交通效率方面实现20.4%的显著改善;较长的交通信号周期与较短的网联人工驾驶汽车驾驶人反应时间有助于协同控制效能的提升。In order to improve the adaption of intelligent vehicle-infrastructure cooperative control methods around connected and signalized intersection to real traffic environment, a novel intelligent vehicle-infrastructure cooperative optimization control method was proposed under the traffic scene of eight-phase connected and signalized intersection mixed intelligent and connected vehicle(ICV) with connected and human-driven vehicle(CHV). Based on modeling the kinematic characteristics and car-following behavior of ICV in the mixed traffic scene, a mixed platoon was formed. A rolling optimization-based cooperative control method of traffic signal and ICV trajectory was proposed based on the platoon model, safety constraints, and fuel consumption model. The cooperative control problem was divided into two layers based on the idea of asynchronous hierarchical optimization, the upper layer was traffic signal timing optimization, and the lower layer was ICV trajectory optimization. Taking the travel time delay and fuel consumption of the vehicle at the intersection as the optimization objectives, the genetic algorithm and three-stage trajectory optimization method were used to solve the traffic signal timing optimization and ICV trajectory optimization, respectively. The stability of the mixed vehicle platoon was verified under different steady-state speeds and penetration rates of ICV. The control effect of the proposed control method and the influence of key parameters on the control effect were analyzed. Analysis results indicate that the proposed control method can effectively improve the traffic efficiency and fuel economy of the intersection under various traffic flows and penetration rates of ICV. In the total ICV environment, the indexes respectively improve by 57.3% and 13.3% when the proposed control method is compared with the method without optimization. Compared with the condition without penetration, with the increase of the penetration rate of ICV, the control efficiency of the proposed control method consta

关 键 词：车路协同 智能网联汽车 信号交叉口 混行交通环境 渗透率 

分 类 号：U491.2[交通运输工程—交通运输规划与管理]